Proactive DDoS Attacks Detection on the Cloud Computing Environment Using Machine Learning Techniques

Abstract

Distributed Denial of Service (DDoS) is a cyber-attack targeted on availability principle of information security by disrupts the services to the users. Cloud computing is very demand service in internet to provide computing resources. DDoS attack is one of the severe cyber-attack to disrupt the resource unavailable to the legitimate users. So DDoS attack detection is more essential in cloud computing environment to reduce the effect of circumstances of the attack. This Chapter proposed DDoS attack detection with network flow features instead of conventional researchers use network type features in cloud computing environment. This study evaluate the DDoS attack detection in cloud computing environment using uncorrelated network type features selected by Pearson, Spearman and Kendall correlation methods. CIC-DDoS2019 dataset used for experiments this study which is collected from Canadian Institute for Cyber Security. Finally, Pearson uncorrelated feature subset produces .better results with KNN and MLP classification algorithms.